Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Nano 12B v2 VL by NVIDIA and Nemotron Nano 9B v2 by NVIDIA represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Nemotron Nano 9B v2 holds a notable pricing advantage for token ingestion. On output generation, Nemotron Nano 9B v2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nemotron Nano 9B v2 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Nemotron Nano 9B v2 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Nemotron Nano 12B v2 VL
开源权重模型NVIDIA 推出的 Nemotron Nano 12B v2 VL 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.12/百万 Token，输出价格为 $0.3/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.12 / 1M
$0.30 / 1M
128K tokens
31%
Nemotron Nano 9B v2
开源权重模型NVIDIA 推出的 Nemotron Nano 9B v2 人工智能模型，具备 131K 上下文窗口，输入价格为 $0.08/百万 Token，输出价格为 $0.2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.08 / 1M
$0.20 / 1M
131.1K tokens
32.5%
核心差异一览
- Nemotron Nano 9B v2 is 1.5x cheaper for API usage (input + output tokens combined).
- Nemotron Nano 9B v2 has a larger context window (131K vs 128K tokens).
- Nemotron Nano 9B v2 is faster at 140 tok/s vs 110 tok/s.
- Nemotron Nano 9B v2 leads on SWE-bench coding (32.5% vs 31.0%).
| Feature | Nemotron Nano 12B v2 VL | Nemotron Nano 9B v2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 42 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.12 / 1M | $0.08 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $0.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 110 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 31% | 32.5% |
GPQA Diamond Graduate-level reasoning | 47% | 48% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.02 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Nano 12B v2 VL · Strengths & Key Advantages
- 由 NVIDIA 研发的高精度 Compact Vision-Language Transformer 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.12/1M，输出 $0.3/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Nemotron Nano 9B v2 · Strengths & Key Advantages
- 由 NVIDIA 研发的高精度 Efficient Dense Transformer 架构，支持 131K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.08/1M，输出 $0.2/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron Nano 12B v2 VL | Nemotron Nano 9B v2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.06 |
Output Tokens / Task Tokens generated per task | 24K | 22K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 8K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Nano 12B v2 VL | Nemotron Nano 9B v2 |
|---|---|---|
| Official API Model ID (SDK) | nemotron-nano-12b-v2-vl | nemotron-nano-9b-v2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-10 | 2025-08 |
| Knowledge Cutoff | August 2025 | June 2025 |
| Architecture | Compact Vision-Language Transformer | Efficient Dense Transformer |
| Parameters | 12B | 9B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.12/1M in) across full 128K window | Flat rate ($0.08/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Cached input at $0.02/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 6 GB VRAM (4-bit Quantized) · 19 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2: 常见问题解答 (FAQ)
Which is more intelligent, Nemotron Nano 12B v2 VL or Nemotron Nano 9B v2?
Nemotron Nano 12B v2 VL is more intelligent. It scores 43 compared with 42 on the composite Intelligence Index.
Which model is faster?
Nemotron Nano 9B v2 is faster, generating 140 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
Nemotron Nano 9B v2 is cheaper at $0.05 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron Nano 9B v2 has lower latency, with a time to first token of 0.28s compared with 0.35s.
Which model has a larger context window?
Nemotron Nano 9B v2 has a larger context window, supporting 131.1K tokens compared with 128K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Nemotron Nano 12B v2 VL if your priority is Robotics perception, automated visual QA, and low-cost multimodal assistants. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron Nano 9B v2 if your workflow prioritizes Robotics control, local PC copilots, and real-time interactive voice agents. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Nemotron Nano 12B v2 VL compare to Nemotron Nano 9B v2 in API token pricing and cost per token?+
Nemotron Nano 12B v2 VL costs $0.12 per 1M input tokens and $0.30 per 1M output tokens, whereas Nemotron Nano 9B v2 costs $0.08 input and $0.20 output. Overall, Nemotron Nano 9B v2 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Nemotron Nano 12B v2 VL or Nemotron Nano 9B v2?+
Nemotron Nano 12B v2 VL supports a context window of 128K tokens (~96k words), while Nemotron Nano 9B v2 supports 131K tokens (~98k words). Therefore, Nemotron Nano 9B v2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron Nano 12B v2 VL or Nemotron Nano 9B v2?+
Nemotron Nano 12B v2 VL scores 31% on SWE-bench Verified while Nemotron Nano 9B v2 scores 32.5%. For autonomous software engineering and multi-file debugging, Nemotron Nano 9B v2 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron Nano 12B v2 VL and Nemotron Nano 9B v2?+
Nemotron Nano 12B v2 VL features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron Nano 9B v2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron Nano 12B v2 VL and Nemotron Nano 9B v2?+
Nemotron Nano 12B v2 VL generates approximately 110 tokens per second, while Nemotron Nano 9B v2 delivers 140 tokens per second. In latency-sensitive production environments, Nemotron Nano 9B v2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron Nano 12B v2 VL and Nemotron Nano 9B v2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron Nano 9B v2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron Nano 12B v2 VL. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs